Understanding oral health and dental care pathways of refugees and asylum seekers in Montreal
Bibliographic record
Abstract
IntroductionApproximately 25,000 refugees and asylum seekers (humanitarian migrants) arrive in Canada each year. The health of this population is fragile, often requiring urgent care upon arrival. We conducted a scoping review in order to understand the burden of oral diseases among humanitarian migrants globally. The only Canadian study we found suggested poor oral health and limited access to oral health care for this population.Humanitarian migrants can benefit from urgent dental care during their first 12 months in Canada. The dental coverage from the Interim Federal Health Program (IFHP) is limited to relief of pain from dental disease or fracture. The policy of the IFHP is subject to amendments that may result in precarious dental coverage for humanitarian migrants. We know little about the oral health awareness and practices of humanitarian migrants and do not understand how they navigate the dental care system in Canada. Further, the roles and experiences of dentists and allied health care providers (e.g., social workers) working with humanitarian migrants in need of oral health care have not received adequate attention from researchers.The purpose of this study was to understand oral health and dental care experiences of humanitarian migrants in Montreal in order to inform policy and services for this population.Objectivesi)To explore pre-migration dental care, current oral health knowledge, practices, and impacts of oral diseases of humanitarian migrants in Montreal;ii)To understand the oral health care process as experienced by humanitarian migrants in Montreal and their perceptions of ways to improve access to oral health care; andiii)To explore the experiences of dentists, social workers, and community leaders working withhumanitarian migrants who needed oral health care in Montreal.MethodologyUsing focused ethnography, grounded in the theories of illness behavior, social exchange theory, and the public health model of the dental care process, I interviewed a purposeful sample of humanitarian migrants who needed dental care; interviews were conducted with an adapted McGill Illness Narrative Interview (MINI) guide. I also observed mobile dental clinics providing care to underserved communities in Montreal. Further, I interviewed a purposeful sample of dentists, social workers, and community leaders working with humanitarian migrants in Montreal. Ethnographic data analysis and interpretation drew upon the MINI and the theories listed above. ResultsI interviewed 37 participants: 25 humanitarian migrants (16 women and 9 men) from four global geographical regions; 5 dentists; 5 social workers; and 2 community leaders. Pre-migration utilization of dental services was mainly for urgent treatment. Once in Canada, participants were cognizant of the causes of oral problems yet oral disease continued to have negative effects on their wellbeing. Participants who received oral health care appreciated the quality; however, the restrictive health care policy, high treatment costs, and long waiting times were barriers to care. Dentists, social workers, and community leaders facilitated the dental care process of humanitarian migrants, although they found it to be a difficult task. Suggestions to improve access to oral health care comprise a more inclusive health care policy, lower costs, public dental insurance, community dental clinics, and oral health promotion and orientation sessions. ConclusionsHumanitarian migrants in this study experienced inadequate oral health care. Their lived experiences help us to identify gaps in the provision of oral health care that should be addressed by local programming and federal policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".